[Inference] Fix API server, test and example (#5712)

* fix api server

* fix generation config

* fix api server

* fix comments

* fix infer hanging bug

* resolve comments, change backend to free port

colossalai/inference/server/api_server.py

@@ -20,10 +20,12 @@ from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse, Response, StreamingResponse
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+import colossalai
 from colossalai.inference.config import InferenceConfig
 from colossalai.inference.server.chat_service import ChatServing
 from colossalai.inference.server.completion_service import CompletionServing
 from colossalai.inference.server.utils import id_generator
+from colossalai.inference.utils import find_available_ports
 
 from colossalai.inference.core.async_engine import AsyncInferenceEngine, InferenceEngine  # noqa
 
@@ -54,8 +56,9 @@ async def generate(request: Request) -> Response:
     """
     request_dict = await request.json()
     prompt = request_dict.pop("prompt")
-    stream = request_dict.pop("stream", "false").lower()
-
+    stream = request_dict.pop("stream", "false")
+    if isinstance(stream, str):
+        stream = stream.lower()
     request_id = id_generator()
     generation_config = get_generation_config(request_dict)
     results = engine.generate(request_id, prompt, generation_config=generation_config)
@@ -66,7 +69,7 @@ async def generate(request: Request) -> Response:
             ret = {"text": request_output[len(prompt) :]}
             yield (json.dumps(ret) + "\0").encode("utf-8")
 
-    if stream == "true":
+    if stream == "true" or stream == True:
         return StreamingResponse(stream_results())
 
     # Non-streaming case
@@ -86,12 +89,14 @@ async def generate(request: Request) -> Response:
 @app.post("/completion")
 async def create_completion(request: Request):
     request_dict = await request.json()
-    stream = request_dict.pop("stream", "false").lower()
+    stream = request_dict.pop("stream", "false")
+    if isinstance(stream, str):
+        stream = stream.lower()
     generation_config = get_generation_config(request_dict)
     result = await completion_serving.create_completion(request, generation_config)
 
     ret = {"request_id": result.request_id, "text": result.output}
-    if stream == "true":
+    if stream == "true" or stream == True:
         return StreamingResponse(content=json.dumps(ret) + "\0", media_type="text/event-stream")
     else:
         return JSONResponse(content=ret)
@@ -101,10 +106,12 @@ async def create_completion(request: Request):
 async def create_chat(request: Request):
     request_dict = await request.json()
 
-    stream = request_dict.get("stream", "false").lower()
+    stream = request_dict.get("stream", "false")
+    if isinstance(stream, str):
+        stream = stream.lower()
     generation_config = get_generation_config(request_dict)
     message = await chat_serving.create_chat(request, generation_config)
-    if stream == "true":
+    if stream == "true" or stream == True:
         return StreamingResponse(content=message, media_type="text/event-stream")
     else:
         ret = {"role": message.role, "text": message.content}
@@ -115,27 +122,29 @@ def get_generation_config(request):
     generation_config = async_engine.engine.generation_config
     for arg in request:
         if hasattr(generation_config, arg):
-            generation_config[arg] = request[arg]
+            setattr(generation_config, arg, request[arg])
     return generation_config
 
 
 def add_engine_config(parser):
-    parser.add_argument("--model", type=str, default="llama2-7b", help="name or path of the huggingface model to use")
-
     parser.add_argument(
-        "--max-model-len",
-        type=int,
-        default=None,
-        help="model context length. If unspecified, " "will be automatically derived from the model.",
+        "-m", "--model", type=str, default="llama2-7b", help="name or path of the huggingface model to use"
     )
-    # Parallel arguments
-    parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1, help="number of tensor parallel replicas")
+    # Parallel arguments not supported now
 
     # KV cache arguments
     parser.add_argument("--block-size", type=int, default=16, choices=[8, 16, 32], help="token block size")
 
     parser.add_argument("--max_batch_size", type=int, default=8, help="maximum number of batch size")
 
+    parser.add_argument("-i", "--max_input_len", type=int, default=128, help="max input length")
+
+    parser.add_argument("-o", "--max_output_len", type=int, default=128, help="max output length")
+
+    parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
+
+    parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
+
     # generation arguments
     parser.add_argument(
         "--prompt_template",
@@ -150,7 +159,7 @@ def parse_args():
     parser = argparse.ArgumentParser(description="Colossal-Inference API server.")
 
     parser.add_argument("--host", type=str, default="127.0.0.1")
-    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--port", type=int, default=8000, help="port of FastAPI server.")
     parser.add_argument("--ssl-keyfile", type=str, default=None)
     parser.add_argument("--ssl-certfile", type=str, default=None)
     parser.add_argument(
@@ -164,6 +173,7 @@ def parse_args():
         "specified, the model name will be the same as "
         "the huggingface name.",
     )
+
     parser.add_argument(
         "--chat-template",
         type=str,
@@ -184,13 +194,21 @@ def parse_args():
 if __name__ == "__main__":
     args = parse_args()
     inference_config = InferenceConfig.from_dict(vars(args))
-    model = AutoModelForCausalLM.from_pretrained(args.model)
     tokenizer = AutoTokenizer.from_pretrained(args.model)
+    colossalai_backend_port = find_available_ports(1)[0]
+    colossalai.launch(
+        rank=0,
+        world_size=1,
+        host=args.host,
+        port=colossalai_backend_port,
+        backend="nccl",
+    )
+    model = AutoModelForCausalLM.from_pretrained(args.model)
     async_engine = AsyncInferenceEngine(
-        start_engine_loop=True, model=model, tokenizer=tokenizer, inference_config=inference_config
+        start_engine_loop=True, model_or_path=model, tokenizer=tokenizer, inference_config=inference_config
     )
     engine = async_engine.engine
-    completion_serving = CompletionServing(async_engine, served_model=model.__class__.__name__)
+    completion_serving = CompletionServing(async_engine, model.__class__.__name__)
     chat_serving = ChatServing(
         async_engine,
         served_model=model.__class__.__name__,